Layered ground truth: Conveying structural and statistical information for document image analysis and evaluation
Bibliographic record
Abstract
This paper addresses the problem of semantic overlap across document objects in the context of ground truth representation for document layout analysis. Document object categories often share primitives from a low-level perspective (e.g. regions inside bars in a bar chart resemble background), making it difficult to evaluate document layout segmentation methods based on pixel classification, as most datasets and ground truth models focus on document objects. We propose a novel ground truth model that utilizes structural and statistical pattern recognition concepts. Statistical pixel-based data derived from low-level elemental patterns are layered onto high-level structural object-based data. We also present evaluation metrics that take advantage of the layered ground truth model, allowing a contextual evaluation of pixel classification algorithms. We apply the proposed model to two recent pixel classification approaches, evaluated on business document images that exhibit a challenging mixture of textual, graphical, and pictorial elements through varied layouts. The proposed model allows to obtain very detailed, comprehensive, and intuitive information on the strengths and limitations of the evaluated approaches that would be impossible to obtain through other models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".